Jean Harb

Reinforcement Learning · 2016–2020

A researcher in the Science corpus: 6 papers; trusts 84 researchers and is trusted by 183 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 120 citations into it; #222 of 2,242 by trust here, #216 by citations
• Artificial Intelligence — 84 citations into it; #1,590 of 35,345 by trust here, #2,208 by citations
• Computational Theory and Mathematics — 20 citations into it; #9,119 of 12,182 by trust here, #6,798 by citations
• Management Science and Operations Research — 15 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it
• Control and Systems Engineering — 10 citations into it
• Electrical and Electronic Engineering — 10 citations into it
• Optimization & Training — 6 citations into it

Whom Jean Harb trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Richard S. Sutton
2. Alex Graves
3. Diederik P. Kingma
4. Volodymyr Mnih
5. Geoffrey E. Hinton
6. Andrew Barto
7. Doina Precup
8. Marc G. Bellemare
9. Sepp Hochreiter
10. J. Urgen Schmidhuber

Who trusts Jean Harb overall

1. Matthew Riemer
2. Sergey Levine
3. Martin Klissarov
4. Khimya Khetarpal
5. Doina Precup
6. Ofir Nachum
7. Miao Liu
8. Shixiang Shane Gu
9. Pieter Abbeel
10. Maximilian Igl

Papers from Jean Harb's perspective

Ordered by how strongly the researchers Jean Harb trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• The Option-Critic Architecture (2016) — 1,311 citations
• Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments (2017) — 6,086 citations
• When Waiting is not an Option : Learning Options with a Deliberation Cost (2017) — 170 citations
• The Barbados 2018 List of Open Issues in Continual Learning (2018) — 12 citations
• Investigating Recurrence and Eligibility Traces in Deep Q-Networks (2017) — 22 citations
• Policy Evaluation Networks (2020) — 42 citations